Quantitative modelling of behaviour

• Explain the basic principles behind discrete choice models
• Classify choice models and discuss their strengths and weaknesses
• Analyse behavioural data using discrete choice models
• Use software to estimate discrete choice models
• Interpret and compare statistical analyses of choice data
• Apply discrete choice models to do demand and market analysis
• Calculate valuation measures and elasticities of relevant attributes
• Argue concerning the usefulness of a specific model for a specific problem
• Write a methodologically sound report containing description of the phenomenon using a descriptive analysis of the data, treatment of data, model estimation and a critical discussion of the results

The course mixes lectures on methodology with exercises and applications of the models to data on behaviour in various contexts. The methodological part introduces the microeconomic framework based on random utility maximization that is fundamental for discrete choice models. Furthermore, it focuses on the most common discrete choice models: multinomial logit models and nested logit model. In addition, we also look at more advanced models and how they can enhance the realism of the modelling. The applications focus on how to apply the models to real as well as hypothetical data. The final part of the course focuses on overall and critical understanding of the methodology through application of the methods in a project.

02402/02403/02323, or similar introduction course to statistics.

Lectures, discussions, theoretical and computer exercises, and a project.